EduVQA: Benchmarking AI-Generated Video Quality Assessment for Education

• URL: http://arxiv.org/abs/2603.03066v1
• Date: Tue, 03 Mar 2026 15:05:27 GMT
• Title: EduVQA: Benchmarking AI-Generated Video Quality Assessment for Education
• Abstract summary: We present EduAIGV-1k, the first benchmark dataset and evaluation framework dedicated to assessing the quality of AI-generated videos (AIGVs)<n>EduAIGV-1k contains 1,130 short videos produced by ten state-of-the-art text-to-video (T2V) models using 113 pedagogy-oriented prompts.
• Score: 21.304581210099045
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While AI-generated content (AIGC) models have achieved remarkable success in generating photorealistic videos, their potential to support visual, story-driven learning in education remains largely untapped. To close this gap, we present EduAIGV-1k, the first benchmark dataset and evaluation framework dedicated to assessing the quality of AI-generated videos (AIGVs) designed to teach foundational math concepts, such as numbers and geometry, to young learners. EduAIGV-1k contains 1,130 short videos produced by ten state-of-the-art text-to-video (T2V) models using 113 pedagogy-oriented prompts. Each video is accompanied by rich, fine-grained annotations along two complementary axes: (1) Perceptual quality, disentangled into spatial and temporal fidelity, and (2) Prompt alignment, labeled at the word-level and sentence-level to quantify the degree to which each mathematical concept in the prompt is accurately grounded in the generated video. These fine-grained annotations transform each video into a multi-dimensional, interpretable supervision signal, far beyond a single quality score. Leveraging this dense feedback, we introduce EduVQA for both perceptual and alignment quality assessment of AIGVs. In particular, we propose a Structured 2D Mixture-of-Experts (S2D-MoE) module, which enhances the dependency between overall quality and each sub-dimension by shared experts and dynamic 2D gating matrix. Extensive experiments show our EduVQA consistently outperforms existing VQA baselines. Both our dataset and code will be publicly available.

Related papers

• Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding [98.3098451637867]
  Video-MME-v2 is a comprehensive benchmark designed to rigorously evaluate the robustness and faithfulness of video understanding.<n>To guarantee data quality, Video-MME-v2 is constructed through a rigorously controlled human annotation pipeline.
  arXiv  Detail & Related papers  (2026-04-06T17:59:56Z)
• VQ-Insight: Teaching VLMs for AI-Generated Video Quality Understanding via Progressive Visual Reinforcement Learning [21.35520258725298]
  VQ-Insight is a novel reasoning-style framework for AIGC video quality assessment.<n>It combines image quality warm-up, general task-specific temporal learning, and joint optimization with the video generation model.<n>It consistently outperforms state-of-the-art baselines in preference comparison, multi-dimension scoring, and natural video scoring.
  arXiv  Detail & Related papers  (2025-06-23T12:20:14Z)
• Towards Generalized Video Quality Assessment: A Weak-to-Strong Learning Paradigm [76.63001244080313]
  Video quality assessment (VQA) seeks to predict the perceptual quality of a video in alignment with human visual perception.<n>The dominant VQA paradigm relies on supervised training with human-labeled datasets.<n>We explore weak-to-strong (W2S) learning as a new paradigm for advancing VQA without reliance on large-scale human-labeled datasets.
  arXiv  Detail & Related papers  (2025-05-06T15:29:32Z)
• GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoning [62.775721264492994]
  GRADEO is one of the first specifically designed video evaluation models.<n>It grades AI-generated videos for explainable scores and assessments through multi-step reasoning.<n> Experiments show that our method aligns better with human evaluations than existing methods.
  arXiv  Detail & Related papers  (2025-03-04T07:04:55Z)
• T2VEval: Benchmark Dataset and Objective Evaluation Method for T2V-generated Videos [9.742383920787413]
  T2VEval is a multi-branch fusion scheme for text-to-video quality evaluation.<n>It assesses videos across three branches: text-video consistency, realness, and technical quality.<n>T2VEval achieves state-of-the-art performance across multiple metrics.
  arXiv  Detail & Related papers  (2025-01-15T03:11:33Z)
• AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM [54.44479359918971]
  We first present AIGVQA-DB, a large-scale dataset comprising 36,576 AIGVs generated by 15 advanced text-to-video models using 1,048 prompts. We then introduce AIGV-Assessor, a novel VQA model that leverages intricate quality attributes to capture precise video quality scores and pair video preferences.
  arXiv  Detail & Related papers  (2024-11-26T08:43:15Z)
• Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model [56.03592388332793]
  We investigate the AIGC-VQA problem, considering both subjective and objective quality assessment perspectives.<n>For the subjective perspective, we construct the Large-scale Generated Video Quality assessment (LGVQ) dataset, consisting of 2,808 AIGC videos.<n>We evaluate the perceptual quality of AIGC videos from three critical dimensions: spatial quality, temporal quality, and text-video alignment.<n>We propose the Unify Generated Video Quality assessment (UGVQ) model, designed to accurately evaluate the multi-dimensional quality of AIGC videos.
  arXiv  Detail & Related papers  (2024-07-31T07:54:26Z)
• CLIPVQA:Video Quality Assessment via CLIP [56.94085651315878]
  We propose an efficient CLIP-based Transformer method for the VQA problem ( CLIPVQA) The proposed CLIPVQA achieves new state-of-the-art VQA performance and up to 37% better generalizability than existing benchmark VQA methods.
  arXiv  Detail & Related papers  (2024-07-06T02:32:28Z)
• Exploring AIGC Video Quality: A Focus on Visual Harmony, Video-Text Consistency and Domain Distribution Gap [4.922783970210658]
  We categorize the assessment of AIGC video quality into three dimensions: visual harmony, video-text consistency, and domain distribution gap. For each dimension, we design specific modules to provide a comprehensive quality assessment of AIGC videos. Our research identifies significant variations in visual quality, fluidity, and style among videos generated by different text-to-video models.
  arXiv  Detail & Related papers  (2024-04-21T08:27:20Z)
• Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment [54.00254267259069]
  We establish the largest-scale Text-to-Video Quality Assessment DataBase (T2VQA-DB) to date. The dataset is composed of 10,000 videos generated by 9 different T2V models. We propose a novel transformer-based model for subjective-aligned Text-to-Video Quality Assessment (T2VQA)
  arXiv  Detail & Related papers  (2024-03-18T16:52:49Z)
• AIGCBench: Comprehensive Evaluation of Image-to-Video Content Generated by AI [1.1035305628305816]
  This paper introduces AIGCBench, a pioneering comprehensive benchmark designed to evaluate a variety of video generation tasks. A varied and open-domain image-text dataset that evaluates different state-of-the-art algorithms under equivalent conditions. We employ a novel text combiner and GPT-4 to create rich text prompts, which are then used to generate images via advanced Text-to-Image models.
  arXiv  Detail & Related papers  (2024-01-03T10:08:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.